(Georgia Institute of Technology, 2012-06)
Wang, Jiuguang; Whitman, Eric C.; Stilman, Mike
We present an optimization-based control strategy
for generating whole-body trajectories for humanoid robots
in order to minimize damage due to falling. In this work,
the falling problem is formulated using optimal control where
we seek to minimize the impulse on impact with the ground,
subject to the full-body dynamics and constraints of the robot
in joint space. We extend previous work in this domain
by numerically approximating the resulting optimal control,
generating open-loop trajectories by solving an equivalent
nonlinear programming problem. Compared to previous results
in falling optimization, the proposed framework is extendable
to more complex dynamic models and generate trajectories
that are guaranteed to be physically feasible. These results
are implemented in simulation using models of dynamically
balancing humanoid robots in several experimental scenarios.
(Georgia Institute of Technology, 2010-05)
Teeyapan, Kasemsit; Wang, Jiuguang; Kunz, Tobias; Stilman, Mike
We present successful control strategies for dynamically
stable robots that avoid low ceilings and other vertical
obstacles in a manner similar to limbo dances. Given the parameters of the mission, including the goal and obstacle
dimensions, our method uses a sequential composition of IO-linearized
controllers and applies stochastic optimization to automatically
compute the best controller gains and references, as
well as the times for switching between the different controllers.
We demonstrate this system through numerical simulations,
validation in a physics-based simulation environment, as well
as on a novel two-wheeled platform. The results show that the
generated control strategies are successful in mission planning
for this challenging problem domain and offer significant
advantages over hand-tuned alternatives.